This file will eventually become your project report for P02: Exploratory Data Analysis. Specifically, you will write rmarkdown to report your exploratory data analysis.
Please see Canvas for more details.
# Example 1: Note relative path, which can be read: Up one
# directory(..), down into source (/source), and
# then "source" an R file (data_access.R)
source("../source/data_access.R")
data_access_test()## [1] "Hello: World!"# Example 1: This function was "sourced" above
msg <- data_access_test(" Morgan!")Hello: Morgan! Hope you have a good day!!
This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.
When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
Chart-2: Purpose:Considering that the nutritional ratio will have a direct impact on the COVID situation from the perspective of immunity, the purpose of making this graph in the early stage is to count the number of COVID cases and deaths around the world and the ratio between them, so as to compare it with each country. Food nutrient ratios are linked to analyze the impact of nutrition on COVID.
observations and insights:According to the scatter plot, in general, almost all countries have a COVID mortality rate below 10%. For some countries with extremely high mortality, such as Belgium(58% death ratio), according to the dataset of Food, the proportion of meat and fruit is far lower than that of countries with low mortality, so it seems that the reason behind this may be because insufficient intake of protein and vitamins leads to decreased immunity.
Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.